Fast, accurate, and racially unbiased pan-cancer tumor-only variant calling with tabular machine learning.

ABSTRACT: Accurately identifying somatic mutations is essential for precision oncology and crucial for calculating tumor-mutational burden (TMB), an important predictor of response to immunotherapy. For tumor-only variant calling (i.e., when the cancer biopsy but not the patient's normal tissue sample is sequenced), accurately distinguishing somatic mutations from germline variants is a challenging problem that, when unaddressed, results in unreliable, biased, and inflated TMB estimates. Here, we apply machine learning to the task of somatic vs germline classification in tumor-only solid tumor samples using TabNet, XGBoost, and LightGBM, three machine-learning models for tabular data. We constructed a training set for supervised classification using features derived exclusively from tumor-only varia

SUBMITTER: McLaughlin RT 

PROVIDER: S-EPMC9825621 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature